1. Field of the Invention
The present invention relates to an apparatus for cardiac events detected in electrograms, EGMs, and to a heart stimulator provided with such an apparatus.
In the following the expression “cardiac event” denotes the depolarization phase in the cardiac cycle, which for atrial signals is commonly known as P wave and for ventricular signals as R wave or QRS complex.
2. Description of the Prior Art
In the field of devices for cardiac rhythm management (CRM), accurate rhythm classification is an increasingly important aspect. Pacemakers are primarily used to assist in bradycardia or when the electrical propagation path is blocked, whereas the primary use of implantable cardioverter defibrillators (ICD) is to terminate ventricular arrhythmia, a life-threatening condition if not immediately treated. In both types of devices, accurate event classification of the electrogram signal is needed for identifying, e.g., atrial and ventricular fibrillation in order to give appropriate therapy for the detected arrhythmia. For pacemakers, this may necessitate changing the pacing mode in order to stabilize the ventricular rhythm during an episode of atrial fibrillation. An ICD responds to ventricular fibrillation by giving a defibrillating shock intended to terminate the fibrillation.
Ever increasing demands are put on both kinds of devices to better handle their primary task as well as to manage other tasks than those originally intended for. One such task may be, for an implantable medical device, to identify atrial flutter in order to terminate it by atrial pacing or to defibrillate atrial fibrillation. Although it is not a life-threatening arrhythmia, atrial fibrillation is an inconvenience to the patient and increases the risk for other diseases such as stroke. Atrial pacing may also be one way of terminating supraventricular tachycardias. An ICD specific task is to identify atrial fibrillation in order to not mistake it for ventricular fibrillation and the risk of giving an unnecessary, and possibly harmful, defibrillation shock. Another, more general, utilization is to efficiently store rhythm data for later analysis and evaluation, already done in modern ICD's. By collecting data, better knowledge of the evolution of cardiac diseases and the functionality of the device can be obtained.
Clustering represents an important task within the classification problem where each individual event is assigned to a cluster of events with similar features. Labelling of the clusters, i.e., associating the cluster with a specific cardiac rhythm, completes the classification such that the device can provide proper therapy when needed. However, certain constraints distinguish clustering in CRM devices from clustering in general. In order to give immediate therapy, it requires clustering to be done in real-time, thus excluding many iterative clustering algorithms such as k-means clustering and competitive learning. Various methods have recently been presented concerning clustering of signals from the surface electrocardiogram (ECG), based on, e.g., self-organizing maps or fuzzy hybrid neural networks, see M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrabdt, and L. Sörnmo, Clustering ECG complexes using Hermite functions and self-organizing maps”, IEEE Trans. Biomed. Eng., vol. 47, pp. 838-848, July 2000, and S. Osowski and T. Linh, “ECG beat recognition using fuzzy hybrid neural network”, IEEE Trans. Biomed. Eng., vol. 48, pp. 1265-1271, November 2001. However, most clustering algorithms used for ECG analysis are computationally rather complex and therefore unsuitable for implantable CRM devices. Furthermore, not much a priori morphologic information is associated with the various rhythms in the electrogram (EGM); this is in contrast to the more well-defined ECG.
Previous work in the area of intracardiac event classification mainly focused on discrimination of a specific condition in order to discern, e.g., atrial fibrillation from other atrial tachyarrythmias, see A. Schoenwald, A. Sahakian, and S. Swiryn, “Discrimination of atrial fibrillation from regular atrial rhythms by spatial precision of local activation direction”, IEEE Trans. Biomed. Eng., vol. 44, pp. 958-963, October 1997. Other applications involve discrimination of ventricular from supraventricular tachycardia, see L. Koyrakh, J. Gillberg, and N. Wood, “Wavelet based algorithms for EGM morphology discrimination for implantable ICDs”, in Proc. Of Comp. In Card. (Piscataway, N.J., USA), pp. 343-346, IEEE, IEEE Press, 1999, and G. Grönefeld, B. Schulte, S. Hohnloser, H.-J. Trappe, T. Korte, C. Stellbrink, W. Jung, M. Meesmann, D. Böcker, D. Grosse—Meininghaus, J. Vogt, and J. Neuzner, “Morphology discrimination: A beat-to-beat algorithm for the discrimination of ventricular from supraventricular tachycardia by implantable cardioverter defibrillators”, J. Pacing Clin. Electrophysiol., vol. 24, pp. 1519-1524, October 2001. More general classification algorithms, which in turn involve training on individual patients, have been based on analog neural networks or wavelet analysis for morphologic discrimination of arrhythmias.
PCT Application WO 97 39 681 describes a defibrillator control system comprising a pattern recognition system. The intracardiac electrogram signal is digitised and delivered for feature selection into a selector. The feature selector outputs selected features to a trained classifier to provide information as to what group the produced signal should be clustered, e.g. ventricular tachycardia. The classifier outputs the classified information for use for a therapeutic decision.
U.S. Pat. No. 5,271,411 discloses an ECG signal analysis and cardiac arrhythmia detection by extraction of features from a scalar signal. A QRS pattern vector is then transformed into features describing the QRS morphology, viz. a QRS feature vector. A normal QRS complex is identified based on the population of QRS complexes located within clusters of QRS features within a feature space having a number of dimensions equal to the number of extracted features. The extracted morphology information is then used for judging whether a heartbeat is normal or abnormal.
U.S. Pat. No. 5,638,823 describes non-invasively detecting of coronary artery diseases. A wavelet transform is performed on an acoustic signal representing one or more sound event caused by turbulence of blood flowing in an artery to provide parameters for a feature vector. This feature vector is used as one input to neural networks, the outputs of which represent a diagnosis of coronary stenosis in a patient.
In Michael A. Unser et al, “Wavelet Applications in Signal and Image Processing IV”, Proceedings SPIE—The International Society for Optical Engineering, 6-9 Aug. 1996, vol. 2825, part two of two parts, pp. 812-821, a wavelet packet based compression scheme for single lead ECGs is disclosed, including QRS clustering and grouping of heart beats of similar structures. For each heart beat detected, its QRS complex is compared to templates of previously established groups. Point-by-point differences are used as similarity measures. The current beat is assigned to the group whose template is most similar, provided predetermined conditions are satisfied. Otherwise a new group is created with the current QRS complex used as the initial group template.